Job Title: Generative AI Engineer

Role Overview

We are seeking experienced Generative AI Engineers to design, build, and productionise advanced AI systems. The role involves developing RAG pipelines, multi-agent workflows, and embedding AI solutions into enterprise environments. Candidates will be responsible for technical delivery from prototype to production, ensuring scalability, reliability, and performance.

Key Responsibilities
Build bespoke Agentic AI solutions and multi-agent workflows.
Integrate core AI models into client environments.
Own technical delivery across multiple deployments from prototype to secure release.
Perform debugging, root-cause analysis, and latency/prompt optimizations.
Implement best practices for LLM evaluation, guardrails, and version control.
Collaborate with product teams and client stakeholders to deliver KPI-driven outcomes.
Flag risks early (model drift, data leakage, system reliability).
Contribute to product roadmap, deployment architectures, and reusable frameworks.

Mandatory Skills
GenAI Ecosystem: RAG pipelines,



multi-agent systems, LangChain, LangGraph, LlamaIndex.
Protocols: Model Context Protocols (MCP), A2A Protocols, Agent Developer Kit (ADK).
LLM Engineering: Prompt engineering, fine-tuning workflows, evaluation.
Data Science Stack: Python, pandas, scikit-learn, PyTorch.
Cloud & MLOps: AWS, Azure, GCP.
DevOps Tools: Docker, Kubernetes, Terraform, CI/CD pipelines, GitHub Actions, GitLab, Jenkins, CircleCI, GitOps.
Backend Development: Python (mandatory), JavaScript/TypeScript (for API integrations).

Preferred Experience
Customer-facing technical deployment experience.
Graduate degree in Computer Science, Engineering, Statistics, or related quantitative discipline.
Robust communication skills to explain technical concepts to diverse audiences.
Proven ability to deliver complex systems in rapid and ambiguous settings.

Interview Details
Mode: Face-to-Face (F2F)
Date: 19-

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